Prevalence of RT-PCR-detected SARS-CoV-2 infection at schools: First

results from the Austrian School-SARS-CoV-2 Study

Peter Willeit, Robert Krause, Bernd Lamprecht, Andrea Berghold, Buck Hanson, Evelyn Stelzl,

Heribert Stoiber, Johannes Zuber, Robert Heinen, Alwin Köhler, David Bernhard, Wegene

Borena, Christian Doppler, Dorothee von Laer, Hannes Schmidt, Johannes Pröll, Ivo Steinmetz,

Michael Wagner

Clinical Epidemiology Team, Department of Neurology, Medical University of Innsbruck,

Innsbruck, Austria and Department of Public Health and Primary Care, University of

Cambridge, Cambridge, UK (P Willeit MD MPhil PhD), Section of Infectious Diseases and

Tropical Medicine, Department of Internal Medicine, Medical University of Graz, Graz,

Austria and BioTechMed-Graz, Graz, Austria (R Krause MD), Department of

Pulmonology, Kepler-University-Hospital, Faculty of Medicine, Johannes Kepler

University Linz, Linz, Austria (B Lamprecht MD), Institute for Medical Informatics,

Statistics and Documentation, Medical University of Graz, Graz, Austria (A Berghold

PhD), Centre for Microbiology and Environmental Systems Science, Department of

Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria (B Hanson

MSc PhD, Dr. H Schmidt, Mag. Dr. M Wagner), Vienna Covid-19 Detection Initiative,

Vienna, Austria (B Hanson MSc PhD, Dr. H Schmidt, Mag. Dr. M Wagner), Diagnostic and

Research Institute of Hygiene, Microbiology and Environmental Medicine, Medical

University of Graz, Graz, Austria (Mag. Dr. E Stelzl, I Steinmetz MD), Institute of Virology,

Medical University Innsbruck, Innsbruck, Austria (Mag. Dr. H Stoiber, W Borena MD

PhD, D von Laer MD), IMP - Research Institute of Molecular Pathology, Vienna, Austria

(J Zuber MD), Gregor Mendel Institute of Molecular Plant Biology, Austrian Academy of

Sciences, Vienna, Austria (Dr. R Heinen), Max Perutz Labs, University of Vienna, Vienna,

Austria (Dr. A Köhler), Center for Medical Research, Faculty of Medicine, Johannes

Kepler University Linz, Linz, Austria (Mag. Dr. D Bernhard, C Doppler MSc PhD, Mag. Dr.

J Pröll), Division of Pathophysiology, Institute of Physiology and Pathophysiology,

Johannes Kepler University Linz, Linz, Austria (Mag. Dr. D Bernhard, C Doppler MSc

PhD), and Center for Microbial Communities, Department of Chemistry and Bioscience,

Aalborg University, Aalborg, Denmark (Mag. Dr. M Wagner)

Correspondence to: Associate Prof Peter Willeit, Medical University of Innsbruck,

Anichstraße 35, 6020 Innsbruck, Austria (peter.willeit@i-med.ac.at) and Prof Michael Wagner,

University of Vienna, Althanstrasse 14, 1090 Vienna, Austria (michael.wagner@univie.ac.at).

3470 words, 38 references, 3 figures, 2 tables, 2 supplementary tables

2

Abstract

Background: There is much debate about the role of schools and children in the SARS-CoV-

2 pandemic. We aimed to quantify reliably the prevalence of SARS-CoV-2 infections at schools

detected with reverse-transcription polymerase-chain-reaction (RT-PCR).

Methods: This nationwide prospective cohort study monitors a representative sample of pupils

(grade 1-8) and teachers at Austrian schools throughout the school year 2020/2021. We

repeatedly test participants for SARS-CoV-2 infection using a gargling solution and RT-PCR.

We herein report on the first two rounds of examinations. We used mixed-effect logistic

regression to estimate odds ratios and robust 95% confidence intervals (95% CI).

Findings: We analysed data on 10734 participants from 245 schools (9465 pupils, 1269

teachers). Prevalence of RT-PCR-detected SARS-CoV-2 infection increased from 0.39% at

round 1 (95% CI 0.28-0.55%, 29 September-22 October 2020) to 1.42% at round 2 (95% CI

1.06-1.90%, 10-16 November). Odds ratios for SARS-CoV-2 infection were 2.29 (95% CI

1.26-4.17, P=0.007) in regions with >500 vs. ≤500 inhabitants/km2, 1.69 (95% CI 1.42-2.00,

P<0.001) for a two-fold higher regional 7-day incidence, and 2.71 (95% CI 1.68-4.39, P<0.001)

in pupils at schools with a high/very high vs. low/moderate social deprivation index.

Associations of community incidence and social deprivation persisted in multivariable models.

There were no differences between age groups, sexes, pupils vs. teachers, or primary (grade 1-

4) vs. secondary schools (grade 5-8).

Interpretation: This monitoring study in Austrian schools revealed SARS-CoV-2 infection in

0.39%-1.42% of participants and identified associations of regional community incidence and

social deprivation with higher prevalence.

Funding: BMBWF Austria.

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Introduction

The SARS-CoV-2 pandemic poses unprecedented challenges on our educational systems.1 As

part of wider strategies to contain the spread of the SARS-CoV-2 virus, many countries have

devised measures at schools with the aim of reducing infection risk. These measures include

adapted in-person learning (e.g. reduced class sizes, staggered time tables, wearing of masks),

complete school closures coupled with virtual learning, or hybrid models.2 School closures

represent a very effective non-pharmaceutical intervention to reduce the transmission of SARS-

CoV-2,3 but have many adverse consequences.1 Thus, there is extensive debate about the role

of schools and children in the SARS-CoV-2 pandemic.1,4

Several prior studies have examined representative samples of the general population to assess

how frequently SARS-CoV-2 infections occur in children compared to adults. For instance,

seroepidemiological studies5–7 showed a somewhat lower prevalence of SARS-CoV-2

antibodies in children than in adults, but it remains unclear whether this difference arises from

reduced exposure related to schools closures, a distinct immune response, or – indeed – reduced

susceptibility. Furthermore, screening studies of the general population in the UK demonstrated

significant time trends in SARS-CoV-2 infection in children over the past months, with a sharp

increase in prevalence in the latest report.8,9 In contrast to these studies in the general

population, evidence from studies with a specific focus on schools is sparse and is largely

restricted to contact-tracing and notification-based studies,10 rather than large-scale screening

of schools for SARS-CoV-2 infection. As children are more likely to be asymptomatic than

adults after a SARS-CoV-2 infection,1 large-scale screening studies are particularly needed for

comprehensively investigating their role in the SARS-CoV-2 pandemic.

We therefore designed the School-SARS-CoV-2 Study, a nationwide prospective cohort study

that screens pupils and teachers at schools in Austria for the occurrence of SARS-CoV-2

infection. We planned to examine study participants repetitively throughout the school year

2020/2021 in 3-5 week intervals. In the present analysis, we analysed data from the first two

rounds of examinations with two main aims: (i) to reliably quantify the prevalence of SARS-

CoV-2 infection detected with reverse-transcription polymerase-chain-reaction (RT-PCR) and

(ii) to determine factors that may be associated with a higher or lower prevalence, thereby

informing upcoming public health policies.

Methods

Study population

The School-SARS-CoV-2 Study is a nationwide prospective cohort study that monitors a

representative sample of pupils and teachers in Austrian schools for presence of RT-PCR-

detectable SARS-CoV-2 infection. Throughout the school year 2020/2021, repeat

measurements are conducted every 3-5 weeks. The present analysis reports on the first two

rounds of examinations. After excluding schools with less than 20 pupils from the sampling

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frame, we randomly selected a total of 250 schools to participate in the study. The selection

process was stratified by federal states, employed selection probabilities proportional to the

numbers of pupils enrolled at the schools, and involved primary schools (grade 1-4) and

secondary schools (grade 5-8).

Within each school, 60 pupils spread across all classes were invited randomly to participate in

the study. In small schools with a total number of pupils less than 60, all pupils at the respective

school were invited. The study population was supplemented with a random selection of

teachers at a target sampling proportion of 1:10, compared to the number of pupils selected at

a school.

The study received ethics approvals by the ethics committees of the Medical University of Graz

(no. 32-672 ex 19/20), Medical University of Innsbruck (no. 1319/2020), the Johannes Kepler

University of Linz (no. 1222/2020), and the University of Vienna (no. 00591/2020). Written

informed consent was obtained from (i) teachers, (ii) participants and their legal representative

for pupils aged 14 years or older, or (iii) their legal representative only for pupils younger than

14 years, according to the ethical approval.

SARS-CoV-2 detection by RT-PCR

To test participants for presence of RT-PCR-detectable SARS-CoV-2 infection, they were

asked to gargle 5 ml of a physiological saline solution or a modified Hank’s balanced salt solution (CaCl2 x 2H2O 1.26 mmol, MgCl2 x 6H2O 0.493mM, MgSO4 x 7H2O 0.41 mM, KCl

5.33 mM, KH2PO4 0.44 mM, NaHCO₃ 4.17 mM, NaCl 137.93 mM, Na2HPO4 0.34 mM, D-

Glucose 5.56 mM) for a total of 60 seconds. Participants were asked not to eat or drink for at

least one hour before gargling. After gargling, the specimen was first transferred to a 50 ml

falcon tube and subsequently pipetted into a sample tube (approximately 700 µl), cooled at a

temperature of 2-8 °C, and transported by courier to one of the four study laboratories for further

analysis within a day. To assure that gargling specimen was collected in a standardised manner,

school doctors, their assistants, and participants received access to training videos and printed

material with detailed step-by-step instructions. Gargling for sample generation is part of the

Austrian test strategy outlined by the Austrian Ministry of Health and is widely applied in

Austria also for diagnostic testing. Gargling has been demonstrated to produce comparable

sample quality like throat swab samples for other respiratory viruses11 and has also been applied

successfully for the detection of SARS-CoV-2.12–15

At the laboratories, sample inactivation, RNA extraction, and RT-qPCR detection of SARS-

CoV-2 was performed according to previously established protocols (for details, see

Supplementary Material). Gargling samples were analysed in pools with a maximal pool size

of 10. Positively tested pools were opened and samples were analysed individually. For all

positively reported samples at least two viral genes were detected. SARS-CoV-2 PCR results

were immediately reported to the study participants and school administrations via text

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messaging and/or email. Whenever a positive test result was obtained, the local health

authorities were also informed instantaneously according to Austrian law.

Collection of additional data on participants and schools

In addition to the exact time point and the SARS-CoV-2 PCR test result, data were recorded on

the participants’ age and sex and – for pupils – the grade and class they currently attend. At the

school level, we collated information on (i) the type of school (primary vs. secondary school),

(ii) the geographical location, (iii) the total number of teachers, pupils, and classes at the school,

(iv) population density at the municipality in which the school is located, and (v) an average

social deprivation index for the pupils attending the school.

The social deprivation index had been ascertained in 2013 using methods described

previously16. In brief, it combined information on four distinct domains: (i) highest level of

education of the pupil’s parents, (ii) current occupation of the pupil’s parents, (iii) migration background, defined as both parents born in a foreign country (OECD definition), and (iv) first

language other than German. Following the recommendation of Bruneforth et al.,17 we

categorised the social deprivation index score, as “low” (score 100-<115), “moderate” (score 115-<125), “high” (score 125-135) and “very high” (score >135). Finally, we obtained data on the regional 7-day incidence of documented COVID-19 cases for all 94 districts in Austria via

the publicly available data and dashboard of the Austrian health authority18 and merged these

data with the school datasets according to districts and time points of the gargling tests.

Statistical methods

In planning the sample size for this study, we estimated the widths of 95% confidence intervals

of SARS-CoV-2 infection prevalences ranging between 0 and 1%, assuming a design effect of

4.0, and considered a sample size involving 11900 pupils and 1200 teachers recruited across

250 schools to afford sufficient statistical power. For instance, based on the assumed design

effect, we expected prevalences of 0.15%, 0.40%, and 0.70% to be associated with 95%

confidence intervals of 0.05-0.36%, 0.21-0.68%, and 0.45-1.05%, respectively.

In descriptive analyses, we summarised categorical variables as counts and percentages and

continuous variables as means and standard deviations (if approximately normally distributed).

We tested whether characteristics of participating schools were associated with each other using

χ2-tests. In analyses of the prevalence of RT-PCR-detectable SARS-CoV-2 infection (ie, overall

prevalence, differences in the prevalence across the two rounds of examinations, and

differences in the prevalence across subgroups), to take into account the clustering of the data

at the school level, we calculated 95% confidence intervals from robust standard errors based

on clustered Sandwich estimators19,20. Participants with a positive test result at round 1 were

censored at round 2, as they are not considered to be at risk after having already experienced

the infection. Odds ratios for RT-PCR-detectable SARS-CoV-2 infection were estimated using

mixed-effect logistic regression models with random intercepts at the participant level. In a first

step, participant and school characteristics were entered in a univariable model. Variables that

6

were associated with RT-PCR-detectable SARS-CoV-2 infection at a significance level of P

≤0.05 were, in a second step, entered concomitantly in a multivariable model. We conducted

analyses with Stata version 14.1 MP. We used two-sided statistical tests and considered P values

≤0.05 as statistically significant.

Role of the funding source

The Federal Ministry of Education, Science and Research of the Republic of Austria funded the

study and supported sample selection, logistics, and assessments at the schools, but had no role

in data analysis or writing of the report. The corresponding authors had full access to all data

in the study and had final responsibility for the decision to submit for publication.

Results

Study population

The flow chart of the schools and participants involved in our study is shown in Figure 1. Of

the 250 schools selected for our study, 245 schools participated (98%). At these schools, a total

of 10957 participants were recruited into our study during the two rounds of examinations.

After exclusion of 223 individuals without a valid gargle test result (2.0%, mostly due to

transfer of insufficient volumes to the test tubes by the school doctors), 10734 individuals were

remained for further analysis.

Key characteristics of schools and participants are provided in Table 1 and Figure 2. The

number of participants was distributed equally across primary and secondary schools (Table

1). At the schools participating in the study, the median total number of pupils was 230

(interquartile range [IQR] 147-331), the median total number of teachers was 27 (IQR 17-43),

and classes consisted – on average – of 21 pupils (IQR 18-23) (Figure 2). Schools recruited a

median of 40 pupils (IQR 29-50) and 6 teachers (IQR 5-6) into our study. On average, teachers

constituted 11.8% of a school’s study sample (IQR 10.0-14.6%).

Supplementary Table 1 illustrates pairwise associations between several school

characteristics. Higher local population density was associated with a greater social deprivation

index (P<0.001) and higher average number of pupils per class (P<0.001). As expected, primary

schools had smaller class sizes than secondary schools (P<0.001).

Prevalence of RT-PCR-detectable SARS-CoV-2 infection

Table 3 presents detailed information on round 1 and 2 of our study. The first round was

conducted between 29 September 2020 and 22 October 2020 and covered 243 schools spanning

across all nine federal states of Austria and resulted in 10156 participants. The median regional

7-day incidence of SARS-CoV-2 cases in the general population at the time of the assessments

was 75 per 100.000 inhabitants (IQR 43-125). The second round was conducted between 10

and 16 November 2020, ending early due to the closure of schools as part of a wider lockdown

7

in Austria employed on 17 November 2020 and resulted in 3745 participants. Round 2 therefore

involved 88 schools only across five federal states of Austria. The corresponding median

regional 7-day incidence in the general population was 419 per 100.000 inhabitants (IQR 392-

641).

At round 1, 40 out of 10156 participants were tested positive, corresponding to a prevalence of

0.39% (95% confidence interval [CI]: 0.28-0.55%) (Table 2). At round 2, 53 additional

participants out of 3745 participants were tested positive. This corresponded to a prevalence of

1.42% (95% CI 1.06-1.90%) and was significantly higher than at round 1 (odds ratio [OR] 3.63,

95% CI 2.36-5.59, P<0.001). At round 1, 209 schools recorded no cases of SARS-CoV-2

infection, 28 schools recorded one case, and 6 schools recorded two cases. At round 2, 52

schools recorded no cases, 23 schools recorded one case, 9 schools recorded two cases, and 4

schools recorded three cases.

Association of participant and school characteristics with SARS-CoV-2 infection

Figure 3 investigated whether participant and school characteristics were associated with the

odds of being tested positive for RT-PCR-detectable SARS-CoV-2 infection. In the unadjusted

model (Figure 3A), significant positive associations were detected for local population density,

regional 7-day incidence in the general population, and the social deprivation index. The ORs

for RT-PCR-detectable SARS-CoV-2 infection were 2.29 for schools located in regions with

>500 vs. ≤500 inhabitants per km2 (95% CI 1.26-4.17, P=0.007), 1.69 for a two-fold higher

regional 7-day incidence (95% CI 1.42-2.00, P<0.001), and 2.71 in pupils at schools with a high

or very high social deprivation index compared to their counterparts (95% CI 1.68-4.39,

P<0.001).

In the multivariable adjusted model involving these three parameters (Figure 3B), only regional

7-day incidence in the general population and the social deprivation index retained statistical

significance, with ORs of 1.66 (95% CI 1.38-1.99, P<0.001) and 2.05 (95% CI 1.23-3.42,

P=0.006), respectively. There was no significant association for local population density in the

multivariable adjusted model (P=0.719).

Discussion

The present study reports on the prevalence of RT-PCR-detectable SARS-CoV-2 infection at

Austrian schools involving a total of 245 schools and 10734 individuals. At the first round of

examinations conducted between 28 September and 22 October 2020, we detected SARS-CoV-

2 infection in 0.39% of the study participants (95% CI 0.25-0.55%). At the second round

conducted between 10 and 16 November, we observed an approximately 3.6-fold higher

prevalence with a point estimate of 1.42% (95% CI 1.06-1.90%). Furthermore, among a range

of participant and school characteristics, regional 7-day incidence in the general population and

social deprivation emerged as relevant factors associated with presence of RT-PCR-detectable

SARS-CoV-2 infection at the participating schools. Collectively, the study provides crucial

8

evidence about the extent and determinants of SARS-CoV-2 infection at schools, thereby

informing decision making about in-person education at Austrian schools and elsewhere in

upcoming months.

To which extent children are inflicted by SARS-CoV-2 and to which extent opening or closing

of schools impacts the dynamics of the SARS-CoV-2 pandemic is much debated.1,4 One aspect

of this debate concerns the occurrence of SARS-CoV-2 infection in children compared to

adults. In our study, we detected SARS-CoV-2 infection in 1.42% (95% CI 1.06-1.90%) of

study participants at the second round of examinations (10-16 November). This prevalence was

somewhat less than the screen-detected prevalence of 2.12% in adults, which was observed by

a different nationwide population-based study21 conducted at a similar time frame (12-14

November, 48 out of 2263 tests positive based on nasopharyngeal swabs). A crude comparison

of the two studies yields a prevalence ratio of 0.67 (95% CI 0.45-0.98, P=0.039), although one

should interpret this ratio with caution due to different testing methods (e.g. gargling vs.

nasopharyngeal swabs, sample pooling only used in the school study) and potential selection

biases (e.g. participation rate 28.9% in the latter study21).

This prevalence ratio is consistent with several population-based studies that investigated

seroprevalence of antibodies to SARS-CoV-2 across the age spectrum.22 For instance, the

seroepidemiological study in Spain ENE-COVID reported a continuous rise in seropositivity

from the youngest age groups (1.1% <1 yr, 2.1% 1-4 yrs, 3.1% 5-9 yrs, 4.0% 10-14 yrs) into

adulthood with a plateau at around 6% at 45 years of age or older.5 Differences by age were

also observed in regional seroprevalence studies in Switzerland (SEROCoV-POP Study6) and

Austria (Ischgl Study7). While these differences may stem from lower susceptibility to SARS-

CoV-2 in children (e.g. due to reduced ACE2 expression in the nasal epithelium23,24), they could

also arise from reduced exposure (e.g. due to school closures) or a milder clinical course of the

infection coupled with a distinct immune response characterised by absence of anti-

nucleocapsid IgG antibodies25. Furthermore, in light of changing policies on school-based

preventive measures and potential seasonal variation, prevalence estimates of SARS-CoV-2

infection in children may be highly volatile. For instance, the latest round of the UK-based

REACT-1 study8 reported that prevalence in children aged 5-17 years had increased sharply

from mid-September to December 2020 and had surpassed the prevalence observed in adults,

with similar time trends reported by the UK Office of National Statistics.9

Our study has identified several factors associated with higher odds of a SARS-CoV-2 case in

the setting of in-person education at schools. The strongest link was observed with regional 7-

day incidence in the general population, associated with 1.66-fold odds (95% CI 1.38-1.99,

P<0.001) for each doubling of community incidence. This observation is in agreement with a

Public Health England report that regional community incidence was significantly associated

with the risk of COVID-19 outbreaks at schools.10 Our study crucially extends the existing

evidence to the months in autumn and to a markedly higher community incidence (median 7-

day incidence 114 vs. ~6 per 100.000). Another important factor associated with higher odds

9

of SARS-CoV-2 cases was social deprivation. While this finding highlights the need for

additional carefully-targeted support of affected children, the underlying mechanisms remain

to be determined. These likely go beyond school-related factors and may include less self-

isolation and physical distancing,26 cramped living conditions,26 and lack of possibilities for

parents to work from home, also coupled with challenges in taking care of a sick child. Of note,

ecological analyses in the US27 and Germany28 have previously shown that deprived areas are

disproportionately affected by SARS-CoV-2. It is also noteworthy that we detected no

significant differences in SARS-CoV-2 prevalence between primary vs. secondary schools,

smaller vs. larger class sizes, pupils vs. teachers, and females vs. males, nor according to the

participants' age.

Another key aspect in assessing the role of schools in the SARS-CoV-2 pandemic is the

infectiousness of children. It is well established that symptomatic children have viral nucleic

acids in their nasopharynx at levels comparable with adults.29,30 SARS-CoV-2 viruses from

children can be cultured in vitro, suggesting that transmission from them is plausible.31 Actual

infectiousness is investigated best in contact tracing studies conducted within schools and/or

households. Some of these studies indicate that children may be somewhat less infectious than

adults.1 Analyses based on notifications of SARS-CoV-2 cases showed low transmission rates

in Ireland before school closures32 and in Australian schools operating at reduced physical

attendance33, but these analyses did not include asymptomatically infected children. In

Germany, after reopening of schools, health authorities identified 2.2 outbreaks per week with

four cases per outbreak on average.34 In England, outbreak analyses largely based on

symptomatic cases showed that staff members were more frequently the seeding case in schools

than students.10 In Austria, health authorities linked 4.3% of the SARS-CoV-2 infections

recorded between 5 October and 15 November 2020 with a traceable source of infection (42%)

to educational settings35, but it should be kept in mind that infected children are often

asymptomatic and that children as contact persons were not systematically tested. A large-scale

household study in South Korea suggested infected children aged 6-11 years were less

contagious than infected older children that were even more contagious than adults.36 It is

important to stress that these reduced transmission rates have been observed against the

backdrop of extensive preventive measures implemented in schools, including reduced class

sizes, staggered time tables, frequent ventilation, wearing of masks, and staying home even

with minimal symptoms.2 In contrast, a report from a US summer camp illustrated the potential

efficient spread in children even if they were younger than 10 years.37 After a 10-day camp

involving various indoor and outdoor activities, including singing and cheering, a staggering

attack rate of 44% was observed among 597 participants and 51% of the children <10 years

were infected. Consistent with this observation, a prospective household study from the US

reported substantial transmission also when the index case was a child.38

The study we presented herein has several major strengths. It evaluated prevalence of SARS-

CoV-2 detection at a time during which in-person education at school was in place nationwide

(for pupils in grade 1-8). Furthermore, it involved a large-scale sample of schools across

10

Austria, thereby enhancing generalisability of our findings. It also has several limitations.

Prevalence of SARS-CoV-2 infections detected in this study likely underestimate the true

burden, since symptomatic individuals (pupils and teachers) or those retained in quarantine

were not present at the time of testing. Also, the second repeat assessment of study participants

was incomplete due to the decision of the Austrian government to close schools for in-person

learning on 17 November 2020. We anticipate restarting repeated screening of the cohort mid-

January and continue assessments until the end of June.

In conclusion, in a large-scale study involving 245 schools in Austria, prevalence of RT-PCR

detected SARS-CoV-2 increased from 0.39% to 1.42% within a period of one month. Higher

community incidence (quantified as the 7-day incidence in the school district) and social

deprivation were associated with higher odds of SARS-CoV-2 at the schools. By determining

SARS-CoV-2 prevalence and identifying potential contributing factors, our study provides

evidence relevant to the decision making about in-person education at Austrian schools and

elsewhere.

Contributors

PW, BL, RK, and MW designed the study. RK, JZ, AK, DvL, HS, IS and MW planned the

logistics and/or laboratory measurements for the study. BH, ES, HS, RH, DB, WB, CD, and JP

performed laboratory measurements. PW and AB performed the statistical analysis. PW, BL,

RK, and MW drafted the manuscript. All authors critically revised the manuscript and agreed

to be accountable for all aspects of the work.

Declaration of interest

The authors report no conflicts of interest in relation to this study.

Acknowledgements

This study was funded by the Federal Ministry of Education, Science and Research of the

Republic of Austria. We are indebted to the study participants as well as the many individuals

involved in study management, participant examination, logistics, and sample analysis that

made this study possible. We thank Angela Wood from the University of Cambridge for her

input on the statistical analysis, and Johanna Trupke and Daniele Soroldoni from the Vienna

BioCenter Core Facilities for molecular biology analytical support. We are grateful to Armin

Haba, Lukas Zaminer, and Nicole Perner at the ‘Federal Institute of Quality Control of the Austrian School System’ (IQS) for their work on the sample selection for this study.

11

References

1 Levinson M, Cevik M, Lipsitch M. Reopening Primary Schools during the Pandemic. N

Engl J Med 2020; 383: 981–85. https://doi.org/10.1056/NEJMms2024920.

2 Lordan R, FitzGerald GA, Grosser T. Reopening schools during COVID-19. Science 2020;

369: 1146. https://doi.org/10.1126/science.abe5765.

3 Li Y, Campbell H, Kulkarni D, et al. The temporal association of introducing and lifting

non-pharmaceutical interventions with the time-varying reproduction number (R) of SARS-

CoV-2: a modelling study across 131 countries. The Lancet Infectious Diseases 2020.

https://doi.org/10.1016/S1473-3099(20)30785-4.

4 Hyde Z. COVID-19, children and schools: overlooked and at risk. Med J Aust 2020; 213:

444-446.e1. https://doi.org/10.5694/mja2.50823.

5 Pollán M, Pérez-Gómez B, Pastor-Barriuso R, et al. Prevalence of SARS-CoV-2 in Spain

(ENE-COVID): a nationwide, population-based seroepidemiological study. The Lancet

2020; 396: 535–44. https://doi.org/10.1016/S0140-6736(20)31483-5.

6 Stringhini S, Wisniak A, Piumatti G, et al. Seroprevalence of anti-SARS-CoV-2 IgG

antibodies in Geneva, Switzerland (SEROCoV-POP): a population-based study. The Lancet

2020; 396: 313–19. https://doi.org/10.1016/S0140-6736(20)31304-0.

7 Knabl L, Mitra T, Kimpel J, et al. High SARS-CoV-2 Seroprevalence in Children and

Adults in the Austrian Ski Resort Ischgl. medRxiv: 2020.09.01.20185884.

https://doi.org/10.1101/2020.08.20.20178533.

8 Riley S, Walters CE, Wang H, et al. REACT-1 round 7 updated report: regional

heterogeneity in changes in prevalence of SARS-CoV-2 infection during the second

national COVID-19 lockdown in England.

https://spiral.imperial.ac.uk/handle/10044/1/84879.

9 Steel K, Donnarumma H. Office for National Statistics 11.12.2020.

10 Ismail SA, Saliba V, Lopez Bernal J, Ramsay ME, Ladhani SN. SARS-CoV-2 infection and

transmission in educational settings: a prospective, cross-sectional analysis of infection

clusters and outbreaks in England. The Lancet Infectious Diseases 2020.

https://doi.org/10.1016/S1473-3099(20)30882-3.

11 Bennett S, Davidson RS, Gunson RN. Comparison of gargle samples and throat swab

samples for the detection of respiratory pathogens. J Virol Methods 2017; 248: 83–86.

https://doi.org/10.1016/j.jviromet.2017.06.010.

12 Malecki M, Lüsebrink J, Teves S, Wendel AF. Pharynx gargle samples are suitable for

SARS-CoV-2 diagnostic use and save personal protective equipment and swabs. Infect

Control Hosp Epidemiol 2020: 1–2. https://doi.org/10.1017/ice.2020.229.

13 Saito M, Adachi E, Yamayoshi S, et al. Gargle Lavage as a Safe and Sensitive Alternative

to Swab Samples to Diagnose COVID-19: A Case Report in Japan. Clinical Infectious

Diseases 2020; 71: 893–94. https://doi.org/10.1093/cid/ciaa377.

14 Guo W-L, Jiang Q, Ye F, et al. Effect of Throat Washings on Detection of 2019 Novel

Coronavirus. Clinical Infectious Diseases 2020; 71: 1980–81.

https://doi.org/10.1093/cid/ciaa416.

12

15 Goldfarb DM, Tilley P, Al-Rawahi GN, et al. Self-collected Saline Gargle Samples as an

Alternative to Healthcare Worker Collected Nasopharyngeal Swabs for COVID-19

Diagnosis in Outpatients, 2020.

16 Pham G, Freunberger R, Robitzsch A, Itzlinger-Bruneforth U, Bruneforth M. Reliabilität

und Stabilität des Index der sozialen Benachteiligung und Kompositionseffekt der Schulen.

Z f Bildungsforsch 2016; 6: 345–64. https://doi.org/10.1007/s35834-016-0164-1.

17 Herzog-Punzenberger B. Nationaler Bildungsbericht Österreich 2012, Band 2: Fokussierte

Analysen bildungspolitischer Schwerpunktthemen. Federal Institute for Quality Assurance

of the Austrian School System (IQS), 2012.

18 Open Government Data. COVID-19: Zeitliche Darstellung von Daten zu Covid19-Fällen je

Bezirk - data.gv.at.

19 Rogers W. Regression standard errors in clustered samples. Stata Technical Bulletin 1994;

3.

20 Williams RL. A Note on Robust Variance Estimation for Cluster-Correlated Data.

Biometrics 2000; 56: 645–46.

21 Presse. https://www.statistik.at/web_de/presse/124846.html (accessed Dec 10, 2020).

22 Viner RM, Mytton OT, Bonell C, et al. Susceptibility to SARS-CoV-2 Infection Among

Children and Adolescents Compared With Adults: A Systematic Review and Meta-analysis.

JAMA Pediatr 2020. https://doi.org/10.1001/jamapediatrics.2020.4573.

23 Bunyavanich S, Do A, Vicencio A. Nasal Gene Expression of Angiotensin-Converting

Enzyme 2 in Children and Adults. JAMA 2020; 323: 2427–29.

https://doi.org/10.1001/jama.2020.8707.

24 Varga Z, Flammer AJ, Steiger P, et al. Endothelial cell infection and endotheliitis in

COVID-19. The Lancet 2020; 395: 1417–18. https://doi.org/10.1016/S0140-

6736(20)30937-5.

25 Weisberg SP, Connors TJ, Zhu Y, et al. Distinct antibody responses to SARS-CoV-2 in

children and adults across the COVID-19 clinical spectrum. Nature immunology 2020.

https://doi.org/10.1038/s41590-020-00826-9.

26 Anderson G, Frank JW, Naylor CD, Wodchis W, Feng P. Using socioeconomics to counter

health disparities arising from the covid-19 pandemic. BMJ 2020; 369: m2149.

https://doi.org/10.1136/bmj.m2149.

27 Mourad A, Turner NA, Baker AW, et al. Social Disadvantage, Politics, and SARS-CoV-2

Trends: A County-Level Analysis of United States Data. Clinical Infectious Diseases 2020.

https://doi.org/10.1093/cid/ciaa1374.

28 Plümper T, Neumayer E. The pandemic predominantly hits poor neighbourhoods? SARS-

CoV-2 infections and COVID-19 fatalities in German districts. European journal of public

health 2020; 30: 1176–80. https://doi.org/10.1093/eurpub/ckaa168.

29 Heald-Sargent T, Muller WJ, Zheng X, Rippe J, Patel AB, Kociolek LK. Age-Related

Differences in Nasopharyngeal Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-

CoV-2) Levels in Patients With Mild to Moderate Coronavirus Disease 2019 (COVID-19).

JAMA Pediatr 2020; 174: 902–03. https://doi.org/10.1001/jamapediatrics.2020.3651.

13

30 Jones TC, Mühlemann B, Veith T, et al. An analysis of SARS-CoV-2 viral load by patient

age, 2020.

31 L'Huillier AG, Torriani G, Pigny F, Kaiser L, Eckerle I. Culture-Competent SARS-CoV-2

in Nasopharynx of Symptomatic Neonates, Children, and Adolescents. Emerging Infect Dis

2020; 26: 2494–97. https://doi.org/10.3201/eid2610.202403.

32 Heavey L, Casey G, Kelly C, Kelly D, McDarby G. No evidence of secondary transmission

of COVID-19 from children attending school in Ireland, 2020. Eurosurveillance 2020; 25:

2000903. https://doi.org/10.2807/1560-7917.ES.2020.25.21.2000903.

33 Macartney K, Quinn HE, Pillsbury AJ, et al. Transmission of SARS-CoV-2 in Australian

educational settings: a prospective cohort study. The Lancet Child & Adolescent Health

2020; 4: 807–16. https://doi.org/10.1016/S2352-4642(20)30251-0.

34 Im Otte Kampe E, Lehfeld A-S, Buda S, Buchholz U, Haas W. Surveillance of COVID-19

school outbreaks, Germany, March to August 2020. Eurosurveillance 2020; 25: 2001645.

https://doi.org/10.2807/1560-7917.ES.2020.25.38.2001645.

35 AGES - Österreichische Agentur für Gesundheit und Ernährungssicherheit.

Epidemiologische Abklärung Covid 19.

https://www.ages.at/themen/krankheitserreger/coronavirus/epidemiologische-abklaerung-

covid-19/ (accessed Dec 11, 2020).

36 Park YJ, Choe YJ, Park O, et al. Contact Tracing during Coronavirus Disease Outbreak,

South Korea, 2020. Emerging Infect Dis 2020; 26: 2465–68.

https://doi.org/10.3201/eid2610.201315.

37 Szablewski CM, Chang KT, Brown MM, et al. SARS-CoV-2 Transmission and Infection

Among Attendees of an Overnight Camp - Georgia, June 2020. MMWR Morb Mortal Wkly

Rep 2020; 69: 1023–25.

38 Grijalva CG, Rolfes MA, Zhu Y, et al. Transmission of SARS-COV-2 Infections in

Households - Tennessee and Wisconsin, April-September 2020. MMWR Morb Mortal Wkly

Rep 2020; 69: 1631–34. https://doi.org/10.15585/mmwr.mm6944e1.

14

Figure 1. Flow-chart of the recruitment into the study.

*Mostly due to transfer of insufficient volumes to the test tubes by the school doctors.

15

Table 1. Descriptive summary of schools and participants investigated as part of

the School-SARS-CoV-2 Study.

No. of schools (%) No. of participants (%)

Total 245 (100.0%) 10734 (100.0%)

Type of school

Primary school 129 (52.7%) 5367 (50.0%)

Secondary school 116 (47.3%) 5367 (50.0%)

Geographical location by federal state*

Burgenland 15 (6.1%) 669 (6.2%)

Carinthia 15 (6.1%) 665 (6.2%)

Lower Austria 46 (18.8%) 2072 (19.3%)

Upper Austria 42 (17.1%) 1716 (16.0%)

Salzburg 15 (6.1%) 610 (5.7%)

Styria 29 (11.8%) 1287 (12.0%)

Tyrol 18 (7.3%) 583 (5.4%)

Vorarlberg 15 (6.1%) 579 (5.4%)

Vienna 50 (20.4%) 2553 (23.8%)

Local population density

≤100 inhabitants/km2 23 (9.4%) 893 (8.3%)

>100-250 inhabitants/km2 47 (19.2%) 1863 (17.4%)

>250-500 inhabitants/km2 34 (13.9%) 1396 (13.0%)

>500-10000 inhabitants/km2 126 (51.4%) 5781 (53.9%)

>10000 inhabitants/km2 15 (6.1%) 801 (7.5%)

Social deprivation index

High/very high 183 (75.6%) 7833 (73.9%)

Low/moderate 59 (24.4%) 2762 (26.1%)

Average no. of pupils/class

≤20 pupils/class 104 (42.4%) 4087 (38.1%)

>20 pupils/class 141 (57.6%) 6647 (61.9%)

*In comparison, the overall number of pupils per federal state were 21056 (3.1%) in Burgenland, 41322 (6.1%) in Carinthia, 128962 (19.0%) in Lower Austria, 120603 (17.8%) in Upper Austria, 42863 (6.3%) in Salzburg, 89844 (13.2%) in Styria, 57570 (8.5%) in Tyrol, 33285(4.9%) in Vorarlberg, and 143617 (21.1%) in Vienna.

16

Figure 2. Total no. of teachers and pupils at participating schools and no. of teachers and pupils

selected to be included in the School-SARS-CoV-2 Study.

IQR denotes interquartile range. Medians and interquartile ranges included in the figure subtitles refer to the overall

study sample (all schools).

17

Table 2. Participant characteristics and prevalence of RT-PCR-detectable SARS-CoV-2

infection at the two rounds of examinations conducted between 28 September and 16 November

2020 within the School-SARS-CoV-2 Study.

Round 1 Round 2 Overall

Assessment of participants

No. of schools 243 88 245

Federal states involved in assessment All Burgenland, Lower Austria, Upper Austria,

Vorarlberg, Vienna

All

Median date of assessment in the year 2020 (range) 12.10. (28.09.-22.10.)

11.11. (10.11.-16.11.)

14.10. (28.09.-16.11.)

Regional 7-day incidence in the general population (per 100,000 inhabitants), median (IQR)

75 (43-125) 419 (392-641) 114 (53-357)

No. of participants 10156 3745 10734

Participants by type of school

No. at primary school (%) 5029 (50%) 2046 (55%) 5367 (50%)

No. at secondary school (%) 5127 (50%) 1699 (45%) 5367 (50%)

Teachers

No. of teachers (%) 1222 (12%) 450 (12%) 1269 (12%)

Age (years), mean (SD) 43.9 (11.8) 44.3 (11.9) 44.0 (11.8)

No. of females (%) 986 (81%) 372 (83%) 1026 (81%)

Pupils

No. of pupils (%) 8934 (88%) 3295 (88%) 9465 (88%)

Age (years), mean (SD) 9.9 (2.4) 9.8 (2.4) 9.8 (2.4)

No. of females (%) 4291 (48%) 1592 (48%) 4541 (48%)

RT-PCR-detectable SARS-CoV-2 infection

No. of cases 40 53 93

Period prevalence (95% CI) 0.39% (0.28-0.55%)

1.42% (1.06-1.90%)

0.87% (0.68-1.11%)

CI denotes confidence interval, SD denotes standard deviation. 95% CI were calculated from robust standard errors

estimated based on clustered Sandwich estimators.

Figure 3. Odds ratios for RT-PCR-detectable SARS-CoV-2 infection at the two rounds of

examinations according to participant and school characteristics in an unadjusted model (Panel A)

and a multivariable adjusted model (Panel B).

CI denotes confidence interval, OR odds ratio, and RT-PCR real-time polymerase chain reaction. The analysis

involved data from round 1 and 2 of the School-SARS-CoV-2 Study. Odds ratios were estimated using mixed-effect

logistic regression models with random intercepts at the participant level. 95% confidence intervals were calculated

from robust standard errors estimated from clustered Sandwich estimators. Variables that were associated with RT-

PCR-detectable SARS-CoV-2 infection at a significant level of ≤0.05 in the unadjusted model (Panel A) were included

in the multivariable adjusted model (Panel B).

Supplementary material

Supplementary methods: RNA extraction and RT-qPCR

At the laboratories, gargle samples in 1 ml FluidX tubes with externally threaded caps (Brooks

Life Sciences, Chelmsford, MA) were organized into 96-well racks and tube barcodes were

scanned (Perception HD, Brooks Life Sciences, Chelmsford, MA). On full racks, positions A1-A4

and E1 were left empty for RNA extraction and RT-qPCR controls. The outside surface of sample

tubes was disinfected by briefly dipping the filled racks into 70% ethanol. Subsequently, the racks

were centrifuged (500 X g for 1 min at room temperature to remove residual ethanol. Thereafter,

sample racks were introduced into a laminar flow hood and sample tubes were decapped using a

FluidX IntelliXcap 96-format Decapper/Capper (Brooks Life Sciences, Chelmsford). Using a

semi-automated Viaflo 96-tip pipettor (Integra Biosciences AG, Zizers Switzerland) and sterile,

300 µl wide-bore filter tips (Integra Biosciences AG, Zizers Switzerland), 12.5 µl of freshly

prepared 2 M 1,4-dithiothreitol (DTT, 5-35 mM final concentration, Carl Roth, GmbH, Karlsruhe,

Germany) were added to each tube and mixed via pipetting to reduce viscosity. Tubes were

recapped and incubated for 10 min at room temperature, followed by centrifugation for 1 min at

500 X g at room temperature to enrich cellular material on the tube bottom. Sample tubes were

again decapped and surplus sample material was removed into a 96 deep-well “waste” plate, until 200 µl of the cell enriched sample fraction remained in each sample tube. The remaining sample

material was gently mixed by pipetting up and down (10 x 180 µl). For pooling of two or more

96-well racks, 50 µl of sample was transferred to a 96 V-bottom deep-well plate (Ritter, Germany)

using the Viaflo pipettor. Additional samples were added as 50 µl aliquots. Pools sizes varied from

3 to 10. Pooling has been used for the diagnosis of influenza1 and has been adapted and employed

successfully in the field of SARS-CoV diagnostics2,3. Pooled samples were homogenized using

mechanical mixing in a KingFisher Flex (ThermoFisher Scientific) with a 96-tip comb for 65 s at

fast speed. One hundred µl of the mixed pool was transferred to a 96 deep-well V-bottom plate

containing 350 µl of lysis buffer (80 mM Tris pH 6.4, 5.7 M guanidinium isothiocyante, 35 mM

EDTA, 2% Triton X-100, 56 mM DTT freshly added) and incubated for 10 min at room

temperature. At this step, 10 µl of RNA extracted from previous positively tested samples were

added in duplicate as sample processing controls to test for RNA degradation and PCR inhibition

(positions A4 and E1; leading to approx. Ct 35 in subsequent RT-qPCR). Automated RNA

extraction was performed using the KingFisher Flex Magnetic Particle Processor System

(ThermoFisher Scientific) or the CyBio Felix System (Analytik Jena) based on carboxylated

magnetic bead separation4 according to the instruction of the manufacturers.

RT-qPCR was performed with 7 µl of extracted RNA using the Luna Universal Probe One-Step

RT-qPCR Kit with the Luna WarmStart RT Enzyme Mix (New England Biolabs, Ipswich, MA)

or the BioRad OnestepRT qPCR Kit according to the manufacturers protocol in a final reaction

volume of 20 µl. RNA extracts from pooled samples were analyzed by duplexing primers and

probes for RP2 (human target; AGATTTGGACCTGCGAGCG,

GCAACAACTGAATAGCCAAGGT, HEX-TTCTGACCTGAAGGCTCTGCGCG–BHQ1)

with orf1b (viral target, TGGGGTTTTACAGGTAACCT,

2

AACACGCTTAACAAAGCACTC, TexasRed-TAGTTGTGATGCAATCATGACTAG-BHQ1)

and UTR (viral target, AGTGTACAGTGAACAATGCT, ATCACATGGGGATAGCACTA

HEX-AGCTGCCTATATGGAAGAGCCCT-BHQ1) with N2 (viral target,

TTACAAACATTGGCCGCAAA, GCGCGACATTCCGAAGAA, FAM-

ACAATTTGCCCCCAGCGCTTCAG-BHQ1) or by triplex PCR (CDC-N2, CDC-ORF1b, CDC-

RP2. Three RT-qPCR reaction controls included: (i) an RNA extract from a previously tested

positive sample (position A1) with a target Ct value of 25 or Twist Synthetic SARS-CoV-2 RNA

Control 2 (10E4 molecules per reaction); (ii) an RNA extract from a previously tested negative

samples (A2); and (iii) a no template control (A3. RT-qPCR reactions were run on a CFX96 Touch

Real-Time PCR Detection System (Bio-Rad, Hercules, CA) with cycling conditions of 55°C for

10 min, 95°C for 1 min and 45 cycles of 95°C for 10 s, and 55°C for 30 s for both duplex PCRs.

Fluorescence signal was recorded at the 55°C annealing and elongation step of each cycle.

Resultant fluorescent curves were analyzed and Ct values were calculated using the CFX Maestro

software (Bio-Rad, Hercules, CA). RNA was extracted from individual samples from positive

pools using 100 µl of remaining DTT-treated original gargle sample as described above. RNA

extracts of unpooled samples were examined using a triplex RT-qPCR targeting N2, orf1b (viral

targets), and RP2 (human target) in 20 µl reactions using the PCR conditions described above and

the Ct values of positive samples were recorded. In some analyses, unpooled samples were

additionally analysed by a duplex PCR (CDC-N1, ORF10).

At the first survey, presence of SARS-CoV-2 RNA of samples send to the Molecular Diagnostics

Laboratory, Diagnostic & Research Institute of Hygiene, Microbiology and Environmental

Medicine at the Medical University of Graz was determined by real-time PCR (qPCR) using the

in vitro diagnostics/Conformité Européenne (IVD/CE)-labeled cobas® SARS-CoV-2 test (Roche

Molecular Systems, Branchburg, NJ, USA) for use on the cobas® 6800/8800 system (Roche

Molecular Diagnostics, Rotkreuz, Switzerland) according to the manufacturer’s instructions

[1,2].5,6 Selective amplification of target nucleic acid from each sample was achieved by the use

of target-specific forward and reverse primers for ORF1a/b nonstructural region that is unique to

SARS-CoV-2. In addition, a conserved region in the structural protein envelope E-gene was

chosen for pan-Sarbecovirus detection. The pan-Sarbecovirus detection set also detect SARS-

CoV-2 virus. An RNA Internal Control, used to monitor the entire preparation and PCR

amplification process, was introduced into each specimen during sample processing. Results

showing inhibition were repeat tested.

At the second survey, a test system consisting of extraction on the KingFisher™ Flex

(ThermoFisher Scientific) instrument using the MagMaX™ Viral/Pathogen Nucleic Acid

Isolation Kit (Life Technologies Corporation, Austin, TX, USA) according to the manufacturer’s instructions and amplification on the CFX96 Touch Real-time PCR Detection System (Bio-Rad)

for detection of SARS-CoV-2 RNA according to the protocol described above (triplex PCR for

individual samples and duplex PCRs for pools) was used. For extraction, an input volume of 200

µl and an elution volume of 50 µl were chosen.

3

References

1 Van TT, Miller J, Warshauer DM, et al. Pooling nasopharyngeal/throat swab specimens to

increase testing capacity for influenza viruses by PCR. J Clin Microbiol 2012; 50: 891–96.

https://doi.org/10.1128/JCM.05631-11.

2 Jones TC, Mühlemann B, Veith T, et al. An analysis of SARS-CoV-2 viral load by patient age,

2020.

3 Yelin I, Aharony N, Tamar ES, et al. Evaluation of COVID-19 RT-qPCR Test in Multi sample

Pools. Clinical Infectious Diseases 2020; 71: 2073–78. https://doi.org/10.1093/cid/ciaa531.

4 Boom R, Sol CJ, Salimans MM, Jansen CL, Wertheim-van Dillen PM, van der Noordaa J.

Rapid and simple method for purification of nucleic acids. J Clin Microbiol 1990; 28: 495–503. https://doi.org/10.1128/JCM.28.3.495-503.1990.

5 Pfefferle S, Reucher S, Nörz D, Lütgehetmann M. Evaluation of a quantitative RT-PCR assay

for the detection of the emerging coronavirus SARS-CoV-2 using a high throughput system.

Eurosurveillance 2020; 25: 2000152. https://doi.org/10.2807/1560-

7917.ES.2020.25.9.2000152.

6 Poljak M, Korva M, Knap Gašper N, et al. Clinical Evaluation of the cobas SARS-CoV-2 Test

and a Diagnostic Platform Switch during 48 Hours in the Midst of the COVID-19 Pandemic.

J Clin Microbiol 2020; 58: e00599-20. https://doi.org/10.1128/JCM.00599-20.

4

Supplementary Table 1. Associations of characteristics of the schools participating in the School-

SARS-CoV-2 Study.

No. of schools within

categories (column %) School type

Social deprivation

index Average class size

Primary

school

Secondary

school

Low/

moderate

High/very

high

≤20 pupils/

class

>20

pupils/

class

Local population density

≤100 inhabitants / km2 17 (13%) 6 (5%) 23 (13%) 0 (0%) 19 (18%) 4 (3%)

>100-250 inhabitants / km2 25 (19%) 22 (19%) 47 (26%) 0 (0%) 30 (30%) 17 (12%)

>250-500 inhabitants / km2 19 (15%) 15 (13%) 30 (16%) 2 (3%) 21 (21%) 13 (9%)

>500-10000 inhabitants / km2 60 (47%) 66 (57%) 80 (44%) 45 (76%) 31 (31%) 95 (67%)

>10000 inhabitants / km2 8 (6%) 7 (6%) 3 (2%) 12 (20%) 3 (3%) 12 (9%)

P=0.231 P<0.001 P<0.001

School type

Primary school 93 (51%) 33 (56%) 77 (74%) 52 (37%)

Secondary school 90 (49%) 26 (44%) 27 (26%) 89 (63%)

P=0.494 P<0.001

Social deprivation index

Low/moderate 85 (83%) 98 (70%)

High/very high 17 (17%) 42 (30%)

P=0.017

P values are from χ2-tests.